Functional and phylogenetic diversity explain different components of diversity effects on biomass production
Bibliographic record
Abstract
The Anthropocene is defined by human‐driven environmental change, with one consequence being the modern dramatic decline in biodiversity globally. This is especially worrisome given the long‐acknowledged causal linkage between biodiversity and ecosystem functioning and the delivery of ecosystem services. However, the exact mechanisms driving biodiversity–ecosystem function (BEF) relationships remain unclear, specifically the linkages between species differences, measured by trait and phylogenetic distances, and how interactions, such as competitive inequality and stable coexistence via niche partitioning, influence these relationships. Using complementary plant biodiversity experiments, a synthetic‐assembled one that combined species in different phylogenetic distance treatments with a semi‐natural functional group removal experiment, we assessed how species differences influence the mechanisms underpinning BEF relationships. We calculated the net biodiversity effect (ΔY) of biomass production and partitioned it into two additive parts: the complementarity and selection effects at species and functional group level to test how phylogenetic diversity and functional diversity capture the influences of the complementarity and selection effects. For both experiments, we found that phylogenetic and functional diversity explained biodiversity effects through similar mechanisms, with a positive relationship with the complementarity effect, and a negative relationship with the selection effect. However, we found that the selection effect was best predicted by a negative relationship with functional dispersion (FD is ) of height where the selection effect was strongest in plots with similarly tall species and weakest with a greater diversity of heights, while higher complementary effects were best explained by increasing phylogenetic diversity (i.e. high MPD a ). Our work revealed that the mechanisms underpinning biodiversity–ecosystem function relationships are dependent on species differences and how these differences influence competitive inequalities and niche differences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".